{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "data_15_filepath = r'/home/baby/AnaFenye/叶片结冰预测/train/15/15_data.csv'\n",
    "data_15_fail = r'/home/baby/AnaFenye/叶片结冰预测/train/15/15_failureInfo.csv'\n",
    "data_15_norm = r'/home/baby/AnaFenye/叶片结冰预测/train/15/15_normalInfo.csv'\n",
    "\n",
    "data_15_df = pd.read_csv(data_15_filepath)\n",
    "data_15_fail_df = pd.read_csv(data_15_fail)\n",
    "data_15_norm_df = pd.read_csv(data_15_norm)\n",
    "\n",
    "flag15 = np.load('flag15.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5467ff93c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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5S5fb7XZW8Xh5uOU65me73WZ2dhaAmZkZ+hmqD0HSQcCHgOdHxN090mwD3Agc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\nwP8HEdd2kMVsu/cAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f54682720b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f54680ee550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f54680949e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5468f8acc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5468403ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5468403a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Xdauk7m3pRJ8xJ3ozKxrX6M3MKsI1ejOzmuo60Uv675I2Sdoo6YosgiqzutcC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\nnBO9mVnFOdGbmVWcE72ZWcX9f1FwYtICW2CHAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5477013e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5477013dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f54684ba518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f54683b7748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f54684a2ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f54683b7470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5467ae7978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5468039f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f54760b9c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f54683b7908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f546821edd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f54683e1f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f546dea4a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f546821e940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5477372898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f546dea4a20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5476f93208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5468258278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5467ebada0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5467eba6d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5467ae7860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f54686425f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5467ae7518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5467a2d6d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5468402390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f54684023c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f546deb5470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f546863fa20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5468826be0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f547870ac88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f546818be48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5478754e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5467919240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5468635438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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9q8h8utAv3LeBQyUdImkv4LXAl6bt8yXgeABJzwa2RsSdSxumLYDofQ+Iczkg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\nEYYuBEkB2QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f54689b5748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f546deafac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f546de8e9b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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DAwP09vYC0NPTQ19fH/39/cDOppdun4ZqxeNpT3s6u1y81apOPEVMDw0NMTIyAsDw8DCT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\nl12MGAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f54681b3630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f546854ab00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "test_data_15 = data_15_df.copy()\n",
    "\n",
    "test_data_15['flag'] = flag15\n",
    "\n",
    "test_data_15\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "plt.figure(1)\n",
    "test_data_15.boxplot(column=['generator_speed'],by='flag')#差别较大\n",
    "plt.show()\n",
    "\n",
    "plt.figure(2)\n",
    "test_data_15.boxplot(column=['wind_speed'],by='flag')#差别较小\n",
    "plt.show()\n",
    "\n",
    "plt.figure(3)\n",
    "test_data_15.boxplot(column=['power'],by='flag')#差别较大\n",
    "plt.show()\n",
    "\n",
    "plt.figure(4)\n",
    "test_data_15.boxplot(column=['wind_direction'],by='flag')#差别较小\n",
    "plt.show()\n",
    "\n",
    "plt.figure(5)\n",
    "test_data_15.boxplot(column=['yaw_position'],by='flag')#差别较小\n",
    "plt.show()\n",
    "\n",
    "plt.figure(6)\n",
    "test_data_15.boxplot(column=['yaw_speed'],by='flag')#差别较小\n",
    "plt.show()\n",
    "\n",
    "plt.figure(7)\n",
    "test_data_15.boxplot(column=['pitch1_angle'],by='flag')#差别较大\n",
    "plt.show()\n",
    "\n",
    "plt.figure(8)\n",
    "test_data_15.boxplot(column=['pitch2_angle'],by='flag')#差别较大\n",
    "plt.show()\n",
    "\n",
    "plt.figure(9)\n",
    "test_data_15.boxplot(column=['pitch3_angle'],by='flag')#差别较大\n",
    "plt.show()\n",
    "\n",
    "plt.figure(10)\n",
    "test_data_15.boxplot(column=['pitch1_speed'],by='flag')#差别较大\n",
    "plt.show()\n",
    "\n",
    "plt.figure(11)\n",
    "test_data_15.boxplot(column=['pitch2_speed'],by='flag')#差别较大\n",
    "plt.show()\n",
    "\n",
    "plt.figure(12)\n",
    "test_data_15.boxplot(column=['pitch1_moto_tmp'],by='flag')#差别较大\n",
    "plt.show()\n",
    "\n",
    "plt.figure(13)\n",
    "test_data_15.boxplot(column=['acc_x'],by='flag')#差别较大\n",
    "plt.show()\n",
    "\n",
    "plt.figure(14)\n",
    "test_data_15.boxplot(column=['acc_y'],by='flag')#差别较小\n",
    "plt.show()\n",
    "\n",
    "plt.figure(15)\n",
    "test_data_15.boxplot(column=['environment_tmp'],by='flag')#差别较大\n",
    "plt.show()\n",
    "\n",
    "plt.figure(16)\n",
    "test_data_15.boxplot(column=['int_tmp'],by='flag')#差别较大\n",
    "plt.show()\n",
    "\n",
    "plt.figure(17)\n",
    "test_data_15.boxplot(column=['pitch1_ng5_tmp'],by='flag')#差别较大\n",
    "plt.show()\n",
    "\n",
    "plt.figure(18)\n",
    "test_data_15.boxplot(column=['pitch2_ng5_tmp'],by='flag')#差别较大\n",
    "plt.show()\n",
    "\n",
    "plt.figure(19)\n",
    "test_data_15.boxplot(column=['pitch3_ng5_tmp'],by='flag')#差别较大\n",
    "plt.show()\n",
    "\n",
    "plt.figure(20)\n",
    "test_data_15.boxplot(column=['pitch1_ng5_DC'],by='flag')#差别较小\n",
    "plt.show()\n",
    "\n",
    "plt.figure(21)\n",
    "test_data_15.boxplot(column=['pitch2_ng5_DC'],by='flag')#差别较大\n",
    "plt.show()\n",
    "\n",
    "plt.figure(22)\n",
    "test_data_15.boxplot(column=['pitch3_ng5_DC'],by='flag')#差别较大\n",
    "plt.show()\n",
    "\n",
    "plt.figure(23)\n",
    "test_data_15.boxplot(column=['group'],by='flag')#差别较大           \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
